Predictive Modeling Applications in Actuarial Science

Volume 1 Description

Volume 1 will lay out the foundations of predictive modeling.

Beginning with reviews of regression and time series methods, this book will provide step-by-step introductions to advanced predictive modeling techniques that are particularly useful in actuarial practice. Readers will gain expertise in several statistical topics, including generalized linear modeling, the analysis of longitudinal, two-part (frequency/severity) and fat-tailed data. Thus, although the audience is primarily professional actuaries, we have in mind a “textbook” approach and so this volume will also be useful for continuing professional development.

To get the most out of this book, readers should have familiarity with multiple linear regression methods such as found in Frees (2010), Regression Modeling with Actuarial and Financial Applications. This book provide the common notation that will be used by chapter authors.



Volume 2 Description

Volume 2 examines applications of predictive modeling. Where Volume 1 developed the foundations of predictive modeling, Volume 2 explores practical uses for techniques, focusing especially on property and casualty insurance. Readers are exposed to a variety of tech- niques in concrete, real-life contexts that demonstrate their value, and the overall value of predictive modeling, for seasoned practicing analysts as well as those just starting out.



Table of Contents

Volume 1

    Introduction

  1. Predictive Modeling in Actuarial Science

    2. Predictive Modeling Foundations

  2. Overview of Linear Models
  3. Regression with Categorical Dependent Variables
  4. Regression with Count Dependent Variables
  5. Generalized Linear Models
  6. Frequency and Severity Models

    7. Predictive Modeling Methods

  7. Longitudinal and Panel Data Models
  8. Linear Mixed Models
  9. Credibility and Regression Modeling
  10. Fat-Tail Regression Models
  11. Spatial Statistics
  12. Unsupervised Learning

    13. Bayesian and Mixed Modeling

  13. Bayesian Computational Methods
  14. Bayesian Regression Models
  15. Generalized Additive Models and Nonparametric Regression
  16. Non-Linear Mixed Models

    17. Longitudinal Modeling

  17. Time Series Analysis
  18. Claims Triangles/Loss Reserves
  19. Survival Models
  20. Transition Modeling


Volume 2

    Generalized Linear Model

  1. Pure Premium Modeling Using Generalized Linear Models
  2. Applying Generalized Linear Models to Insurance Data: Frequency/Severity versus Pure Premium Modeling
  3. Generalized Linear Models as Predictive Claim Models

    4. Extensions of the Generalized Linear Model

  4. Frameworks for General Insurance Ratemaking: Beyond the Generalized Linear Model
  5. Using Multilevel Modeling for Group Health Insurance Ratemaking: A Case Study from the Egyptian Market

    6. Unsupervised Predictive Modeling Methods

  6. Clustering in General Insurance Pricing
  7. Application of Two Unsupervised Learning Techniques to Questionable Claims: PRIDIT and Random Forest

    8. Applications on Current Problems in Actuarial Science

  8. The Predictive Distribution of Loss Reserve Estimates over a Finite Time Horizon
  9. Finite Mixture Model and Workers’ Compensation Large-Loss Regression Analysis
  10. A Framework for Managing Claim Escalation Using Predictive Modeling
  11. Predictive Modeling for Usage-Based Auto Insurance